Support vector machines (SVMs) based optimization framework is presented for the data‐driven optimization of numerically infeasible differential algebraic equations (DAEs) without the full discretization of the underlying first‐principles model. By formulating the stability constraint of the numerical integration of a DAE system as a supervised classification problem, we are able to demonstrate that SVMs can accurately map the boundary of numerical infeasibility. The necessity of this data‐driven approach is demonstrated on a two‐dimensional motivating example, where highly accurate SVM models are trained, validated, and tested using the data collected from the numerical integration of DAEs. Furthermore, this methodology is extended and tested for a multidimensional case study from reaction engineering (i.e., thermal cracking of natural gas liquids). The data‐driven optimization of this complex case study is explored through integrating the SVM models with a constrained global grey‐box optimization algorithm, namely the ARGONAUT framework.

中文翻译：

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具有数值不可行性的微分代数方程的数据驱动优化算法。

提出了基于支持向量机 (SVM) 的优化框架，用于对数值不可行的微分代数方程 (DAE) 进行数据驱动优化，而无需对底层第一原理模型进行完全离散化。通过将 DAE 系统数值积分的稳定性约束公式化为监督分类问题，我们能够证明 SVM 可以准确地映射数值不可行性的边界。这种数据驱动方法的必要性在二维激励示例中得到了证明，其中使用从 DAE 的数值积分收集的数据训练、验证和测试高度准确的 SVM 模型。此外，该方法还针对反应工程的多维案例研究进行了扩展和测试（即，天然气液体的热裂解）。通过将 SVM 模型与受约束的全局灰盒优化算法（即 ARGONAUT 框架）相结合，探索了这个复杂案例研究的数据驱动优化。